Instructions to use crypticvandal/alpamayo-vision-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use crypticvandal/alpamayo-vision-v3 with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("crypticvandal/alpamayo-vision-v3") - Notebooks
- Google Colab
- Kaggle
Alpamayo Vision Model v3
Overview
Vision model trained on NVIDIA Physical AI Autonomous Vehicles dataset for trajectory prediction in autonomous driving scenarios.
Training Details
- Dataset: nvidia/PhysicalAI-Autonomous-Vehicles (streamed)
- Training steps: 1000
- Batch size: 4
- Architecture: CNN (4 conv layers) + GlobalAvgPool + Dense + 3 heads
- Input: 5-frame sequence, 256x256 RGB, camera_front_wide_120fov
- Output: trajectory (20x2), lane center offset, heading angle
- Optimizer: Adam (lr=1e-4)
- Best trajectory error: 15.74
Usage
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